Instructions to use ModelTC/bart-base-cola with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ModelTC/bart-base-cola with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ModelTC/bart-base-cola")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ModelTC/bart-base-cola") model = AutoModelForSequenceClassification.from_pretrained("ModelTC/bart-base-cola", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from ModelTC/bart-base-cola: direct link, hf CLI and curl.
- Browser
- Download file 2.99 kB
-
https://huggingface.co/ModelTC/bart-base-cola/resolve/main/training_args.bin
- Command line
-
hf download hf://ModelTC/bart-base-cola/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/ModelTC/bart-base-cola/resolve/main/training_args.bin
2.99 kB
- Xet hash:
- 71859e8b119734e3fb28832bd23fa96c72713d20a73a0cf7ac506a5c03c013f1
- Size of remote file:
- 2.99 kB
- SHA256:
- a21a436b6e8570df92e245a5b6f68672b3b1e04d27eed2a48a376a683ef2c570
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